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caculator.py
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caculator.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Sep 29 10:48:53 2020
@author: Bing
"""
import re
import numpy as np
import time
import pandas as pd
from IsoSpecPy import IsoSpecPy
def fragtomz(str1):
str1=str1.replace("Cl","A")
dd={"C":12,"H":1.007825032,"O":15.99491462,"N":14.003074,"S":31.972071,"P":30.97376163,"A":34.96885268,"F":18.99840322}
ptn=re.compile('([A-Z])([\d]*)')
frag=ptn.findall(str1)
mz=0
for code, nums in frag:
if code in dd:
if(nums==""):
num=1
else:
num=int(nums)
mz=mz+dd[code]*num
return mz+0.00055
def formula_cac(mz,error):
formulas=[]
formulas2=[]
formulas3=[]
mz_act=[]
error_ab=[]
error_re=[]
error_re2=[]
rdb=[]
for c in range(2,min(int(mz/12)+2,51)):
for f in range(3,min(int((mz-c*12)/18.9984)+3,51)):
for o in range(min(int((mz-12*c-18.9984*f)/15.9949)+2,21)):
for s in range(min(int((mz-12*c-18.9984*f-15.9949*o)/31.9721)+2,3)):
for n in range(min(int((mz-12*c-18.9984*f-15.9949*o-s*31.9721)/14.0031)+2,6)):
for p in range(min(int((mz-12*c-18.99840322*f-15.99491462*o-s*31.972072071-n*14.003074)/30.97376)+2,3)):
for h in range(min(int((mz-12*c-18.99840322*f-15.99491462*o-s*31.972072071-n*14.003074-p*30.97376163)/1.007825032)+2,101)):
rdb_2=h+f-p-n+1
if(rdb_2%2==1):
continue
rdb_=c+1-0.5*rdb_2
if(-0.5<rdb_<20):
mz_=c*12+f*18.99840322+o*15.99491462+s*31.972071+n*14.003074+p*30.97376163+h*1.007825032+0.00055
ab_error=mz-mz_
re_error=(ab_error)/mz*1000000
re_error2=abs(re_error)
if(re_error2<=error):
mz_act.append(mz_)
error_ab.append(ab_error*1000)
error_re.append(re_error)
rdb.append(rdb_)
formula="C"+str(c)
formula2="C"+str(c)
if(h!=0):
formula+="H"
formula2+="H"+str(h)
formula3=formula+str(h+1)
if(h!=1):
formula+=str(h)
else:
formula3=formula+"H"
if(f!=0):
formula+="F"
formula3+="F"
formula2+="F"+str(f)
if(f!=1):
formula+=str(f)
formula3+=str(f)
if(n!=0):
formula+="N"
formula3+="N"
formula2+="N"+str(n)
if(n!=1):
formula+=str(n)
formula3+=str(n)
if(o!=0):
formula+="O"
formula3+="O"
formula2+="O"+str(o)
if(o!=1):
formula+=str(o)
formula3+=str(o)
if(p!=0):
formula+="P"
formula3+="P"
formula2+="P"+str(p)
if(p!=1):
formula+=str(p)
formula3+=str(p)
if(s!=0):
formula+="S"
formula3+="S"
formula2+="S"+str(s)
if(s!=1):
formula+=str(s)
formula3+=str(s)
formulas.append(formula)
formulas2.append(formula2)
formulas3.append(formula3)
error_re2.append(re_error2)
df=pd.DataFrame({"Formula":formulas,"m/z":mz_act,"Error(ppm)":error_re,"Error(mDa)":error_ab,"RDB":rdb,"abs_re_error":error_re2,"Formula_1":formulas2,"Formula_mole":formulas3})
df=df.sort_values(by="abs_re_error",ascending=True)
df=df.reset_index(drop=True)
df=df[["Formula","m/z","Error(ppm)","Error(mDa)","RDB","Formula_1","Formula_mole"]]
return df
def formula_cac_cl(mz,error=5):
formulas=[]
formulas2=[]
formulas3=[]
mz_act=[]
error_ab=[]
error_re=[]
error_re2=[]
rdb=[]
for cl in range(1,5):
for c in range(2,min(int((mz-cl*34.96885268)/12)+2,51)):
for f in range(3,min(int((mz-c*12-cl*34.96885268)/18.9984)+3,51)):
for o in range(min(int((mz-12*c-cl*34.96885268-18.9984*f)/15.9949)+2,21)):
for s in range(min(int((mz-12*c-cl*34.96885268-18.9984*f-15.9949*o)/31.9721)+2,3)):
for n in range(min(int((mz-12*c-cl*34.96885268-18.9984*f-15.9949*o-s*31.9721)/14.0031)+2,6)):
for p in range(min(int((mz-12*c-cl*34.96885268-18.99840322*f-15.99491462*o-s*31.972072071-n*14.003074)/30.97376)+2,3)):
for h in range(min(int((mz-12*c-cl*34.96885268-18.99840322*f-15.99491462*o-s*31.972072071-n*14.003074-p*30.97376163)/1.007825032)+2,101)):
rdb_2=h+cl+f-p-n+1
if(rdb_2%2==1):
continue
rdb_=c+1-0.5*rdb_2
if(-0.5<rdb_<20):
mz_=c*12+f*18.99840322+o*15.99491462+s*31.972071+n*14.003074+h*1.007825032+cl*34.96885268+p*30.97376163+0.00055
ab_error=mz-mz_
re_error=(ab_error)/mz*1000000
re_error2=abs(re_error)
if(re_error2<=error):
mz_act.append(mz_)
error_ab.append(ab_error)
error_re.append(re_error)
rdb.append(rdb_)
formula="C"+str(c)
formula2="C"+str(c)
if(h!=0):
formula+="H"
formula2+="H"+str(h)
formula3=formula+str(h+1)
if(h!=1):
formula+=str(h)
else:
formula3=formula+"H"
if(cl!=0):
formula+="Cl"
formula3+="Cl"
formula2+="Cl"+str(cl)
if(cl!=1):
formula+=str(cl)
formula3+=str(cl)
if(f!=0):
formula+="F"
formula3+="F"
formula2+="F"+str(f)
if(f!=1):
formula+=str(f)
formula3+=str(f)
if(n!=0):
formula+="N"
formula3+="N"
formula2+="N"+str(n)
if(n!=1):
formula+=str(n)
formula3+=str(n)
if(o!=0):
formula+="O"
formula3+="O"
formula2+="O"+str(o)
if(o!=1):
formula+=str(o)
formula3+=str(o)
if(p!=0):
formula+="P"
formula3+="P"
formula2+="P"+str(p)
if(p!=1):
formula+=str(p)
formula3+=str(p)
if(s!=0):
formula+="S"
formula3+="S"
formula2+="S"+str(s)
if(s!=1):
formula+=str(s)
formula3+=str(s)
formulas.append(formula)
formulas2.append(formula2)
formulas3.append(formula3)
error_re2.append(re_error2)
df=pd.DataFrame({"Formula":formulas,"m/z":mz_act,"Error(ppm)":error_re,"Error(mDa)":error_ab,"RDB":rdb,"abs_re_error":error_re2,"Formula_1":formulas2,"Formula_mole":formulas3})
df=df.sort_values(by="abs_re_error",ascending=True)
df=df.reset_index(drop=True)
df=df[["Formula","m/z","Error(ppm)","Error(mDa)","RDB","Formula_1","Formula_mole"]]
return df
def formula_caculator(MS1spe,mz,error_ppm=5,iso_thre=0.8):
MS1=MS1clean(MS1spe,mz,0.002)
if(len(MS1)==0):
return pd.DataFrame()
cl_=is_Cl(MS1)
if(cl_):
df_formula=formula_cac_cl(mz,error_ppm)
else:
df_formula=formula_cac(mz,error_ppm)
i=0
iso_list=[]
MS1=pd.DataFrame(MS1)
MS1.columns=["mass","intensity"]
while(i<len(df_formula)):
MS1_exp=isotope_pattern(df_formula["Formula_1"][i])
iso_score=compare_isotope(MS1,MS1_exp)
if(iso_score>iso_thre):
i=i+1
iso_list.append(iso_score)
else:
df_formula.drop([i],inplace=True)
df_formula=df_formula.reset_index(drop=True)
df_formula["iso_score"]=iso_list
return df_formula
def isotope_pattern(formula, thres=0.99):
isotope = IsoSpecPy.IsoSpec.IsoFromFormula(formula, thres)
isotope = isotope.getConfsNumpy()
output = pd.DataFrame({'mass': isotope[0]+0.00055, 'intensity': np.exp(isotope[1])})
return output
def compare_isotope(measured, expected, error=0.002):
measured['intensity'] = measured['intensity']/measured['intensity'][0]
expected['intensity'] = expected['intensity']/expected['intensity'][0]
expected_m1 = sum(expected['intensity'][(expected['mass'] > expected['mass'][0] - error + 0.997) & (expected['mass'] < expected['mass'][0] + error + 1.006)])
measured_m1 = sum(measured['intensity'][(measured['mass'] > measured['mass'][0] - error + 0.997) & (measured['mass'] < measured['mass'][0] + error + 1.006)])
expected_m2 = sum(expected['intensity'][(expected['mass'] > expected['mass'][0] - error + 1.994) & (expected['mass'] < expected['mass'][0] + error + 2.013)])
measured_m2 = sum(measured['intensity'][(measured['mass'] > measured['mass'][0] - error + 1.994) & (measured['mass'] < measured['mass'][0] + error + 2.013)])
expected_m3 = sum(expected['intensity'][(expected['mass'] > expected['mass'][0] - error + 2.991) & (expected['mass'] < expected['mass'][0] + error + 3.018)])
measured_m3 = sum(measured['intensity'][(measured['mass'] > measured['mass'][0] - error + 2.991) & (measured['mass'] < measured['mass'][0] + error + 3.018)])
score = (1 - abs(expected_m1 - measured_m1)) * (1 - abs(expected_m2 - measured_m2)) * (1 - abs(expected_m3 - measured_m3))
return score
def MS1clean(MS1spe,mz,error=0.002):
while(MS1spe[0,0]<(mz-error)):
MS1spe=np.delete(MS1spe,0,0)
if(len(MS1spe)==0):
break
return MS1spe
def is_Cl(MS1spe,error=0.002,tol=0.25):
MS1=pd.DataFrame(MS1spe)
MS1.columns=["mass","intensity"]
MS1['intensity'] =MS1['intensity']/MS1['intensity'][0]
MS1_m2 = sum(MS1['intensity'][(MS1['mass'] > MS1['mass'][0] - error + 1.994) & (MS1['mass'] < MS1['mass'][0] + error + 2.013)])
if(MS1_m2<0.25):
return 0
else:
return 1
def formulatolist(formula):
formula=formula.replace("Cl","A")
ptn=re.compile('([A-Z])([\d]*)')
frag=ptn.findall(formula)
list1=[]
list2=[]
list3=[]
for i in range(len(frag)):
list1.append(frag[i][0])
if(frag[i][1]==""):
list2.append(1)
else:
list2.append(int(frag[i][1]))
if("C" in list1):
list3.append(list2[list1.index("C")])
else:
list3.append(0)
if("H" in list1):
list3.append(list2[list1.index("H")])
else:
list3.append(0)
if("A" in list1):
list3.append(list2[list1.index("A")])
else:
list3.append(0)
if("F" in list1):
list3.append(list2[list1.index("F")])
else:
list3.append(0)
if("N" in list1):
list3.append(list2[list1.index("N")])
else:
list3.append(0)
if("O" in list1):
list3.append(list2[list1.index("O")])
else:
list3.append(0)
if("P" in list1):
list3.append(list2[list1.index("P")])
else:
list3.append(0)
if("S" in list1):
list3.append(list2[list1.index("S")])
else:
list3.append(0)
return list3
def listtoformula(list_):
formula=""
list2=["C","H","Cl","F","N","O","P","S"]
for i in range(len(list_)):
if(list_[i]!=0):
formula=formula+list2[i]+str(list_[i])
return formula
# caculate the number and kind of atoms changed after the transformtion, the transformed formula was also returned
def trans_pfas(cac_formula,database_formula,num_thre):
list_cac=formulatolist(cac_formula)
list_data=formulatolist(database_formula)
count1=0
count2=0
list3=[]
for i in range(len(list_cac)):
list3.append(list_cac[i]-list_data[i])
count1=count1+abs(list3[i])
if(count1>num_thre):
return -1,-1,-1,""
if(list3[i]!=0):
count2=count2+1
return count1,count2,abs(list3[3]),listtoformula(list3)
# judge the change of atom number is higher than num_thre or not
def trans_pfas2(cac_formula,database_formula,num_thre):
list_cac=formulatolist(cac_formula)
list_data=formulatolist(database_formula)
count1=0
list3=[]
for i in range(len(list_cac)):
list3.append(list_cac[i]-list_data[i])
count1=count1+abs(list3[i])
if(count1>num_thre):
return 0
return 1
# return formula change
def trans_pfas3(cac_formula,database_formula):
list_cac=formulatolist(cac_formula)
list_data=formulatolist(database_formula)
list1=[]
for i in range(len(list_cac)):
list1.append(list_cac[i]-list_data[i])
return listtoformula(list1)